P02.08 The role of the inflammasome in the spatiotemporal evolution of the immune cell landscape in post-resection glioblastoma
Notice bibliographique
Résumé
<h3>Background</h3> Glioblastomas (GB) are the most severe and deadliest brain tumors in adults. Survival is estimated < 15 months after diagnosis and with a relapse rate > 95%. The current standard-of-care involves surgery, when possible, and radiotherapy coupled with chemotherapy. Two characteristics might underlie the high relapse rate in GB: 1) the infiltrative capacity of tumor cells that spread out of the hypoxic and acidic tumor core, and 2) the unique composition of the tumor immune microenvironment (TME) that is sparce in T lymphocytes and natural killer (NK) cells but dominated by glioma-associated macrophages (GAMs). Although surgery is a standard treatment in GB, it fails to remove infiltrative tumor cells and causes an inflammatory and immunosuppressive trauma that might promote GB recurrence by altering the TME. However, the post-resection diversity of immune cells in GB and the pathways that determine their functions in primary growth versus post-resection recurrence remain largely unknown. In this project, we characterize the immune landscape of GB before and after surgical resection and explore the role of the inflammasome in its dynamics and regulation. <h3>Materials and Methods</h3> GL261-GFP-GLuc mesenchymal-type GB cells were orthotopically injected in WT or inflammasome-deficient(<i>Ice<sup>-/-</sup></i>) mice. On day 18 post-implantation, tumors and adjacent parenchyma tissue were collected from the unresected group (group 1). In parallel, tumor resection was performed on a second group of mice (group 2). 10 days later, tumors and adjacent parenchyma tissue were collected from group 2. Following tissue dissociation, immune cells were FACS-sorted from GB tumor-bearing mouse brains. Sorted immune cells were multiplexed using barcoaded lipid indices into 6 different pools and ScRNAseq (10x Genomics) was performed. For the scRNAseq, 30,000 cells/pool, corresponding to 7,500 viable cells/sample were loaded on the 10x chip. <h3>Results</h3> Following putative doublet removal and exclusion of stressed or dead cells, we analysed the transcriptomes of ~61,000 single immune cells. Following data integration with Seurat, community detection, non-linear dimension reduction and graph clustering, 23 Louvain clusters were identified, including 15 from the myeloid lineage and 8 from the lymphoid lineage. we observed a significant depletion of microglia (MG)/MG-TAM from the tumor compared to the adjacent non-tumoral parenchyma, which was accompanied by a significant influx of bone-marrow-derived (BM)-TAM and monocytes as already known. Little differences were observed between WT or <i>Ice<sup>-/-</sup></i>mice before resection. However, post-resection remodelling of the GB TME was regulated by the inflammasome. Notably, monocytes, dendritic cells and regulatory T cells (Treg) subsets increased post resection in the adjacent non-tumoral tissue in WT but not <i>Ice<sup>-/-</sup></i>mice. Similarly, the intra-tumoral influx of Treg and the compositional changes of BM-TAMs observed in WT mice were blunted in inflammasome-deficient conditions. These TME differences correlated with faster tumor regrowth and decreased survival rates in WT mice compared to inflammasome-deficient mice. <h3>Conclusion</h3> Our data reveal a significant impact of GB resection on TME remodeling and implicate the inflammasome in post-resection recurrence. <h3>Disclosure Information</h3> <b>S. Lillo:</b> None. <b>D. Chalopin:</b> None. <b>M. Derieppe:</b> None. <b>J. Martineau:</b> None. <b>J. Giraud:</b> None. <b>A. Le Dantec:</b> None. <b>O. Mollier:</b> None. <b>M. Nikolski:</b> None. <b>T. Daubon:</b> None. <b>M. Saleh:</b> None.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».